Behavioral coding focuses on deriving higher-level behavioral annotations using observational data of human interactions. Automatically identifying salient events in the observed signal data could lead to a deeper understanding of how specific events in an interaction correspond to the perceived high-level behaviors of the subjects. In this paper, we analyze a corpus of married couples' interactions, in which a number of relevant behaviors, e.g., level of acceptance, were manually coded at the session-level. We propose a multiple instance learning approach called Diverse Density Support Vector Machines, trained with acoustic features, to classify extreme cases of these behaviors, e.g., low acceptance vs. high acceptance. This method has the benefit of identifying salient behavioral events within the interactions, which is demonstrated by comparable classification performance to traditional SVMs while using only a subset of the events from the interactions for classification.
Bibliographic reference. Gibson, James / Katsamanis, Athanasios / Black, Matthew P. / Narayanan, Shrikanth (2011): "Automatic identification of salient acoustic instances in couples' behavioral interactions using diverse density support vector machines", In INTERSPEECH-2011, 1561-1564.